Notes Frame

Institutional support for the program has been provided by the Department of Sociology and the Racial and the Ethnic Studies Institute at Texas A&M University, College Station, Texas.

SimSeg Lite is a browser-based computer simulation of residential segregation dynamics in urban areas. It is geared for educational use and is intended to be a tool for helping students gain a better understanding of certain aspects of social processes and conditions that contribute to the creation and maintenance of residential segregation in urban areas.

SimSeg Lite allows users to explore prevailing theories of residential segregation by designing and running simulation experiments that implement processes and conditions emphasized by different theories. The results of the simulation experiments provide insights into questions about how different social dynamics and urban-demographic conditions may (or may not) affect residential segregation (at least in the context of the SimSeg Lite model). An overview of how to run experiments can be found at the following link:

SimSeg Lite is a Java Applet. It is a limited-capability adaptation of SimSeg -- a more capable stand-alone program that runs under Windows.

SimSeg Lite is geared to users with a limited background in segregation measurement and theory. The stand-alone version of SimSeg implements a more sophisticated and flexible simulation model and is geared to users with a stronger background in segregation measurement and theory . It may be of interest to users who wish to move beyond casual exploration of segregation dynamics. The program can be obtained from the following web site:

The chief advantages to using SimSeg Lite are that it is readily accessible since it can be invoked through a java-enabled browser by anyone with access to the world wide web. In addition, because it implements a simple simulation model with only a few variables, it is relatively easy to learn and use.

The chief disadvantage of using SimSeg Lite is the "flip side" of this last point; it implements a simulation model that is easier to learn and use because it is simpler and less nuanced than the one implemented in SimSeg (the full version of the program). In addition, SimSeg Lite's execution speed is much slower (an inherent limitation of java applets). This places practical constraints on the amount of computational complexity that can be incorporated into the applet version of the simulation model.

Finally, SimSeg Lite lacks many useful features found in the full SimSeg program. For example, the full program provides the user the capability to: observe a more detailed visual depiction of the evolution of the city landscape over the course of the simulaton experiment; create and run new simulation scenarios; save and "replay" simulation results; examine a much wider range of simulation results; create much larger simulated cities; and run multiple simulations (included repeated trials of the same simulation) in "batch" mode.

4. If needed, click on Continue as many times as needed to continue running the simulation until the segregation pattern becomes evident. (This will be a matter of judgement, but in many cases the pattern is evident within 30 cycles.)

Results Form Click on this link to view a form for systematically recording results of simulation experiments. (Note, the document is in Adobe Acrobat format.)

Analysis Guide Click on this link to view a document that provides guidelines for performing simple analyses to test hypotheses about the effects that different factors may have on ethnic segregation. (Note, the document is in Adobe Acrobat format.)

Some Basic Research Questions Click on this link to view a document that provides some suggestions for basic questions to investigate using the SimSeg Lite program.

More Advanced Research Questions Click on this link to view a document that provides some examples of how more advanced research questions can be investigated using the SimSeg Lite program.

SimSeg is a stand-alone computer simulation program for Windows that models selected factors thought to influence patterns of residential segregation in US urban areas.

SimSeg is SimSeg Lite's "big brother". It is a much more capable and ambitious program that implements a more nuanced and sophisticated simulation model. It models the role of factors such as:

1. Demographic structures including the city's ethnic mix, the shape of the status distribution, and inter-group inequality in socioeconomic status;

2. Urban structure in the form of the spatial distribution of high-quality housing;

3. The role of household-level location decisions as guided by preferences for housing quality, neighborhood status, and ethnic mix and constrained by means; and

Version 2.0 of the stand-alone version of the SimSeg program may be downloaded for personal and educational use. The distribution files can be obtained by going to the following link:

Version 2.0 is the last version of SimSeg to be placed in the public domain. A significantly more capable version of the program is currently under development with funding support from NIH. Dr. Richard Senft of Amber Waves Software of Lancaster, PA and Dr. Mark Fossett of Texas A&M University-College Station, TX are leading this effort.

The final version of the new implementation (final version) is scheduled to be completed by 2004. It will be licensed and distributed commercially for educational and professional use. In addition to being more capable, it will be more "polished" and easier to use. It also will be supported by Amber Waves Software, a professional software development company with special expertise in developing and supporting scientific simulation models.

Default Scenario

Means Testing (M1). Under this setting moves are not "means tested". Households are not subject to meeting qualifying standards before moving to higher-quality housing units.

Ethnic Inequality (I1). Under this setting all ethnic groups have identical status distributions.

Area Stratification (A1). Under this setting area stratification is low as higher-quality housing is distributed evenly across all neighborhoods.

SES/HQ Goals (S1). Under this setting households are not motivated by goals of seeking high-quality housing and higher-status neighborhoods when making location decisions.

Discrimination (D1). Under this setting discrimination is not active and thus does not impede the movement of minority households.

Ethnic Preferences (P1). Under this setting households do not consider area ethnic mix when making location decisions.

Neaby Areas (N1). Under this setting households do not consider the ethnic mix in nearby areas when making location decisions.

Overview. The default scenario provides no systematic forces for producing ethnic segregation. Thus, the only segregation that will emerge under this simulation scenario is that produced by random forces.

Use the selection boxes to change the simulation scenario and explore what kinds of segregation patterns are produced under different scenario settings.

Designed Scenarios

These pre-designed scenarios can be interesting in their own right (they are chosen to reflect popular hypotheses about forces creating residential segregation). Or they can be a convenient first step toward specifying a user-defined scenario.

No Segregation Forces. Scenario 1 is the default scenario. It is one devoid of segregating forces. It is useful for establishing baseline conditions of segregation that results from chance rather than systematic forces.

Economic Forces Only. Scenario 2 reflects economic forces commonly hypothesized to produce ethnic segregation. These include minority disadvantage in means to pay for high-quality housing (inequality), high area stratification, means-testing of moves, and households that seek high-quality housing and high-status neighborhoods,

Discrimination Only. Scenario 3 reflects the hypothesis that segregation is produced by housing discrimination. It includes high levels of white prejudice and discrimination.

Ethnic Preferences Only. Scenario 4 reflects the hypothesis that segregation is produced by the voluntary choices of households that seek out areas with particular ethnic mixes. The ethnic preferences implemented here correspond to those reported in recent multi-group surveys.

Economics & Discrimination. Scenario 5 combines economic forces and discrimination.

Economics & Preferences. Scenario 6 combines economic forces with ethnic preferences reported in multi-group surveys.

Discrimination & Preferences. Scenario 7 combines discrimination with ethnic preferences reported in multi-group surveys.

Economics, Discrimination, & Preferences. Scenario 8 combines economic forces with discrimination and ethnic preferences reported in surveys.

User-Defined Scenarios. Any scenario that does not exactly match one of the eight pre-designed scenarios listed above.

Dimensions of Segregation

The literature on measuring residential segregation recognizes five separate dimensions of segregation: uneven distribution, isolation, clustering, centralization, and concentration.

Uneven Distribution. This reflects the extent to which groups are distributed unevenly across areas of the city. The point of reference is even distribution which obtains when a group's percentage representation is the same in every neighborhood as it is in the city as a whole.

SimSeg measures uneven distribution using the index of dissimilarity - the most common measure of departure from even distribution. It ranges from 0 (exact evenness) to 100 (maximum departure from evenness).

Isolation. This reflects the degree to which members of a group are residentially isolated from other groups. This is affected by both uneven distribution and by relative group size. Thus, since whites are 60% of the population, it is difficult for the group to get a low clustering score.

SimSeg measures isolation using a "P*" measure that registers the average residential contact that members of a group have with other members of their own group. It ranges from 0 to 100 (all contact is within group).

Clustering. This reflects the degree to which the areas where a group is predominant occur near each other in urban space and form "clusters" or "ghettos" - distinct macro regions of contiguous neighborhoods where the group is predominant. The opposite of clustering is "checkerboarding" - a pattern wherein ethnic neighborhoods are randomly distributed throughout the city.

SimSeg Lite measures clustering as the percentage of households residing within "ethnic clusters". Households are classified as residing within an ethnic cluster based on two criteria. The first concerns the ethnic mix of the immediate area of residence. The co-ethnic presence (i.e., same group representation) in the area must be at least 60%.

The second criteria concerns the ethnic mix in nearby areas. Here one of two conditions must be met. At least three contiguous areas must each separately meet the 60% co-ethnic presence standard. Or, there must be at least 50% co-ethnic representation in the combined population of all contiguous areas.

These measures may range from 0 (no clustering) to 100 (maximum possible clustering. Like Isolation scores, clustering scores are shaped both by patterns of uneven distribution and by relative group size. Thus, since whites are 60% of the population, it is difficult for the group to get a low clustering score.

Centralization. This reflects the degree to which members of a group are concentrated in neighborhoods near the center of the city.

SimSeg measures centralization on a scale ranging from -100 to 100 with the following interpretation

100 maximum centralization
    50 very high centralization
    20 high centralization
     0 neutral distribution
   -20 low centralization
   -50 very low centralization
  -100 minimum centralization

1. Each housing unit is assigned a percentile score for distance from the city center (e.g., 60 = farther from the city center than 60% of all housing units).

2. The mean (Y) for these percentile distance scores is calculated separately for each ethnic group.

3. These mean scores (Y) are converted to centralization scores (CS) using the following formula:

The formula can be explained as follows. The term (50-Y) captures deviation from neutral distribution (since 50 is the expected percentile distance score under random distribution).

The term (100-P) is included in the formula to adjust for the fact that the minimum and maximum scores for Y are P/2 and 100-(P/2), repectively. Thus, the maximum range of Y for any group is 100-P.

The term (2*100) is a scaling adjustment to convert the result into a number between -100 and 100. (Without the adjustment, the range would be -0.5 to 0.5.)

Concentration. This reflects the degree to which a members of a group are concentrated in a small geographic area due to disproportionate representation in high-density neighborhoods.

Since all neighborhoods in the SimSeg Lite simulation have the same density (i.e., all neighborhoods are identical in size and have an identical number of housing units), this dimension of segregation is not measured here.

Other Terms & Concepts

Households are the "active" agents in the simulation. Each household is assigned an ethnic status and a socioeconomic status. Each household also is assigned preferences governing (a) their goals for residing in high-quality housing, (b) their goals for residing in high-status neighborhoods, and (c) their goals for achieving specific levels of in-group and out-group contact with ethnic groups.

Housing units are located throughout the city. They are assigned a level of "quality" for the duration of the simulation. The scale for "quality" parallels the scale for household socioeconomic status (i.e., it is a percentile score between 0 and 100). Thus, the city-wide distribution of housing values is "rectangular".

Households gain access to high-quality housing through a search process that may involve "means-tested" (i.e., households may be screened to determine whether they can "afford" high-quality homes before they can move to them.

Depending on the simulation scenario, the city's urban structure may vary in terms of the distribution of high-quality and low-quality housing along a city-to-suburb gradient.

When urban structure includes area stratification, the city is characterized by a pattern in which higher-quality housing is concentrated in outlying neighborhoods and lower-quality housing is concentrated in inner neighborhoods. Otherwise, higher- and lower-quality housing is randomly distributed throughout the city.

Housing units are arranged in a spatial grid or city "landscape". The city landscape is divided into equal-sized, square subareas with distinct boundaries. These are termed "bounded neighborhoods" and are roughly comparable to census block groups.

Bounded nedighborhoods are the areal units that households consider when they evaluate "neighborhood". These are also the areal units that are used to compute city-wide segregation segregation measures (e.g., the index of dissimilarity).

The legend below illustrates how SimSeg Lite uses color and shading to indicate the ethnicity and socioeconomic status of the households residing in occupied housing units.

Household Search

During each cycle a certain fraction of households are randomly chosen and given the opportunity to "search" for new housing.

The searching household is "shown" a random selection of vacant housing units (the selections may be "means tested" to make sure the household can "afford" the unit). The household then identifies the "best" available unit and makes a decision to move to that unit or remain in their present unit.

Means-Testing Moves

When "Means-Testing" is active, the "available" houses "shown" to searching households are "means-tested" to assure that the searching household can "afford" the housing unit.

If active, households will only be shown housing units whose value is no higher than 5 points above the household's status. (Housing value and households status are measured on the same 1-99 scale.)

In addition, households will first be shown housing units whose value is no less than 25 points below the household's status. If enough available units are not identified, the search is progresssively expanded to include all housing units regardless of value.

Socioeconomic Status

Households are assigned a socioeconomic status (SES) score between 0 and 100. The scores reflect "percentile position" in the city's status distribution. Thus, the distribtion is "rectangular".

Color "shading" is used to signify SES; brighter shades indicate higher SES, dimmer shades indicate lower SES. Examine the legend for an illustration of this pattern. [Legend]

Display Options

Use this selection box to choose what characteristic of the housing unit or the resident household will be displayed in the city landscape image. There are five options to choose from:

Ethnicity & SES. Depicts resident households using color and shading to indicate both ethnicity and socioeconomic status. This is the default option.

Ethnicity Only. Depicts resident households using color to indicate ethnic status. (Socioeconomic status is not indicated.)

SES Only. Depicts resident households using a gray-scale to indicate socioeconomic status. (Ethnic status is not indicated.)

House Value. Depicts housing units using a gray-scale to indicate the value of the house.

Distance Rings. Depicts housing units using a gray-scale to indicate distance from the city center. Each ring contains 33% of the city's housing units.

Ethnic Status

Households are assigned membership in one of three ethnic groups - white, black, or Hispanic. In the context of the simulations, these ethnic group labels are merely that - arbitrary labels devoid of content other than that explicitly outlined by the simulation scenario.

The only characteristics that households have (beyond ethnic status) are socioeconomic status and ethnic preferences. Thus, these are the only dimensions on which ethnic groups may differ.

Color "hue" is used to signify ethnic status - white households are depicted in cyan, black households in magenta, and Hispanic households in olive. Examine the legend for an illustration of this pattern. [Legend]

Decision to Move

A household's decision to move is based on an evaluation of the available housing identified through search.

Each unit is evaluated on the basis of up to three factors: (1) the quality of the housing unit, (2) the average status of the area, and (3) the ethnic mix of the area (and possibly nearby areas as well). Which factors are salient in any given simulation are determined by the model's settings.

An overall "attractiveness" score is computed from the simple average of separate attractiveness scores computed for each factor that is "relevant" in the simulation.

If the best available house identified through search is more attractive than the household's current house, the household attempts to move. Whether it is successful may depend on whether discrimination is active or not.

In a certain fraction of searches, the household moves to the best available house identified through search even if it is less attractive than the household's current house. This simulates the fact that households frequently "must" move. For example, they may lose their lease; they may be forced to relocate due to a job transfer; they experience a major change in household structure (e.g., divorce, death), etc., etc..
Household Preferences

Households in the simulation are given "preferences" that guide their evaluation of their current residence and also available alternatives (when the household is searching for housing). Three preferences may be active:

-Housing Quality

-Neighborhood Status

-Neighborhood Ethnic Mix

Preferences relating to "housing quality" and "neighborhood status" are determined by the setting of the "S" selection box. Preferences relating to ethnic mix are determined by the setting of the "P & N" selection boxes.

Overview of Preferences

Housing Quality

Neighborhood SES

Neighborhood Ethnic Mix

Discrimination

Minority households may encounter discrimination when they search for housing and/or when they attempt to move to a new residence.

Regarding search, minority households may be less likely to be "shown" housing in predominantly white neighborhoods and more likely to be "shown" housing in minority neighborhoods.

Regarding movement, minority households may be subject to "exclusion" when they attempt to move into predominantly white neighborhoods.

The chances of discrimination increases with the strength of the discrimination setting and with the white representation in the neighborhood.

Cycles & Stages

"Cycles" and "stages" represent the time dimension in the simulation model.

Cycles are measured and reported. A cycle is a period of time during which a certain fraction of the households in the city are selected at random and given the opportunity to engage in search and possibly move. The fraction is chosen to produce movement that approximates the amount of movement seen in real cities over a period of 6 months to one year. (Early in the simulation the amount of movement is actually higher because all households are required to move at least once.)

Choosing a higher number of cycles helps insure that the simulation will run long enough for segregation patterns to emerge and become evident. Choosing a lower number makes it easier to "pause" and observe intermediate results as the pattern begins to emerge.

A "stage" is a span of cycles that are run using the same scenario (i.e., the same settings on the model variables). If the scenario is changed and new cycles are run, a new stage has begun.

Five of the seven model variables can be changed during a simulation experiment. Two - ethnic inequality and area stratification - cannot be changed in "mid-experiment". When they are changed, the simulation must be "reset" before it can be run with the new settings.

Help Topics

SimSeg Lite

Credits

Support

Default Scenario

Designed Scenarios

Dimensions of Segregation

Other Terms & Concepts